Torch_DDP_Example

torch_ddp.py

import os
import tempfile

import torch
from torch import nn
from torch.nn.parallel import DistributedDataParallel

import ray
from ray.train import Checkpoint, CheckpointConfig, RunConfig, ScalingConfig
from ray.train.torch import TorchTrainer

# If using GPUs, set this to True.
use_gpu = True
# Number of processes to run training on.
num_workers = 8

hidden_dim = 32000

# Define your network structure.
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.layer1 = nn.Linear(1, hidden_dim)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(hidden_dim, 1)

    def forward(self, input):
        return self.layer2(self.relu(self.layer1(input)))

# Training loop.
def train_loop_per_worker(config):

    # Read configurations.
    lr = config["lr"]
    batch_size = config["batch_size"]
    num_epochs = config["num_epochs"]

    # Fetch training dataset.
    train_dataset_shard = ray.train.get_dataset_shard("train")

    # Instantiate and prepare model for training.
    model = NeuralNetwork()
    model = ray.train.torch.prepare_model(model)

    # Define loss and optimizer.
    loss_fn = nn.MSELoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=lr)

    # Create data loader.
    dataloader = train_dataset_shard.iter_torch_batches(
        batch_size=batch_size, dtypes=torch.float
    )

    # Train multiple epochs.
    for epoch in range(num_epochs):

        # Train epoch.
        for batch in dataloader:
            output = model(batch["input"])
            loss = loss_fn(output, batch["label"])
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        # Create checkpoint.
        base_model = (model.module
            if isinstance(model, DistributedDataParallel) else model)
        checkpoint_dir = tempfile.mkdtemp()
        torch.save(
            {"model_state_dict": base_model.state_dict()},
            os.path.join(checkpoint_dir, "model.pt"),
        )
        checkpoint = Checkpoint.from_directory(checkpoint_dir)

        # Report metrics and checkpoint.
        ray.train.report({"loss": loss.item()}, checkpoint=checkpoint)

# $ANYSCALE_ARTIFACT_STORAGE
storage_path = "s3://anyscale-staging-data-cld-kvedzwag2qa8i5bjxuevf5i7/org_7c1Kalm9WcX2bNIjW53GUT/cld_kvedZWag2qA8i5BjxUevf5i7/artifact_storage/test"

# Define configurations.
train_loop_config = {"num_epochs": 20, "lr": 0.01, "batch_size": 32}
scaling_config = ScalingConfig(num_workers=num_workers, use_gpu=use_gpu)
run_config = RunConfig(checkpoint_config=CheckpointConfig(num_to_keep=1), storage_path=storage_path)

# Define datasets.
train_dataset = ray.data.from_items(
    [{"input": [x], "label": [2 * x + 1]} for x in range(2000)]
)
datasets = {"train": train_dataset}

# Initialize the Trainer.
trainer = TorchTrainer(
    train_loop_per_worker=train_loop_per_worker,
    train_loop_config=train_loop_config,
    scaling_config=scaling_config,
    run_config=run_config,
    datasets=datasets
)

# Train the model.
result = trainer.fit()

# Inspect the results.
final_loss = result.metrics["loss"]